EEG Derivation Research Articles

Automated insomnia detection using wavelet scattering network technique with single-channel EEG signals

Sleep is crucial for both the physical and mental well-being of human life. As the sleep pattern varies in every individual, it is essential to develop a methodology that enables us to detect sleep-related disorders precisely. Insomnia is one such disorder that affects humans mentally and physically. Signals from Polysomnograms (PSGs) are typically used to identify different sleep disorders. The PSG signals need a lot of handling time and are not patient-friendly. This work proposes to develop a method to automatically identify insomnia using single-channel Electroencephalogram (EEG) signals. We have employed a Wavelet Scattering Network (WSN), a variant of deep convolutional networks, to extract features effectively WSN is an optimized network capable of learning features that assist in discriminating patterns concealed within signals. In addition, WSNs are insensitive to local disturbances, enhancing the network’s dependability and efficiency. We have used single-channel EEG derivation, C4-A1, to create six separate subsets variants based on the sleep-stage annotations, developing the model individually and collectively for the wake, N1, N2, N3, and Rapid Eye Movement (REM) sleep stages To build the proposed model, 12,576 epochs from the Cyclic Alternating Pattern (CAP) sleep database and 20,523 epochs from the Sleep Disorders Research Center (SDRC) dataset were considered. Our proposed model has attained the highest classification Accuracy (Acc) of 97% and area under the curve (AUC) of 0.98 using a Trilayered Neural Network (TNN) classifier considering the wake-sleep stage of the CAP database. Our devised method is straightforward and computationally efficient. Hence, it could be used for clinical applications.

Електроенцефалограми у дітей з сенсорною та руховою деприваціями

Sensorineural hearing loss and cerebral palsy (CP) are natural models of deprivation. In individuals with these pathologies, the rhythmic components of the electroencephalogram (EEG) of individual zones provide an opportunity to identify the central mechanisms of adaptive changes that develop at the system level. The normalized spectral power of EEG derivation was used in our study to assess the effect of auditory and motor deprivation on the functional state of the brain. The study involved 240 children aged 8 to 15 years who were divided into four groups: group I included 40 muffled boys and 42 girls; to II - 40 guys and 40 girls who heard; to III - 40 boys and 38 girls with impaired motor activity; to IV - 50 guys and 50 girls with normal physical activity. We found that children with limited motor activity had a lower normalized spectral power of the δ-rhythm in the posterior leads on the right side (respectively, girls - 27.23 ± 0.84 and 33.99 ± 1.94; boys - 27.80 ± 1.78 and 36.82 ± 2.04) and temporal from the left (respectively, girls - 27.40 ± 0.98 and 37.82 ± 1.66; boys - 39.88 ± 1.63 and 42.17 ± 2.04). An increase in the normalized spectral power of the θ-rhythm in the temporo-parietal-occipital region of the brain (respectively, girls – 25.73 ± 1.18 and 13.64 ± 0.47; boys – 19.86 ± 0.95 and 12.74 ± 0.78,), a lower normalized spectral power of the α-rhythm and a greater normalized spectral power of the β-rhythm (α-rhythm: respectively, girls – 24.34 ± 1.54 and 41.71 ± 2.54; boys – 27.84 ± 1.48 and 45.01 ± 2.41; β-rhythm: girls – 25.00 ± 1.14 and 9.95 ± 0.48; boys – 19.94 ± 0.86 and 9.20 ± 0.66) over almost the entire scalp compared to children with sensorineural hearing loss.

Multichannel search strategy for improving the detection of auditory steady-state response.

Auditory steady-state response (ASSR) is useful for hearing threshold estimation. The ASSR is usually detected with objective response detectors (ORD). The performance of these detectors depends on the signal-to-noise ratio (SNR) as well as the signal length. Since it is undesirable to increase the signal length, then, this work provides a multivariate technique for improving the SNR and consequently the detection power. We propose the insertion of a short calibration step before the detection protocol, in order to perform a search among the available electroencephalogram (EEG) derivations and select the derivation with the highest SNR. The ORD used in this work was the magnitude-squared coherence (MSC). The standard detection protocol is to use the same EEG derivation in all exams. Using 22-scalp positions, the new technique achieved a detection rate higher than that obtained in 99.13% of the standard detection protocol. When restrictions were applied to the search, a superior performance was achieved. Thus, the technique proposed was able to track the best EEG derivations before exams and seems to be able to deal with the variability between individuals and between sessions.

Sleep spindle activity in childhood onset schizophrenia: Diminished and associated with clinical symptoms

Neuroimaging studies of childhood onset schizophrenia (COS), a rare yet severe form of schizophrenia with an onset before the age of 13 years, have shown continuity with adult onset schizophrenia. Previous research in adult patients has shown reduced sleep spindle activity, transient oscillations in the sleep electroencephalogram (EEG) generated through thalamocortical loops. The current study examines sleep spindle activity in patients with COS. Seventeen children and adolescents with COS (16 years ±6.6) underwent overnight sleep EEG recordings. Sleep spindle activity was compared between patients with COS and age and gender matched controls and correlated with clinical symptom severity. We found pronounced deficits in sleep spindle amplitude, duration, density and frequency in patients with COS (effect size = 0.61 to 1.96; dependent on metric and EEG derivation). Non-rapid eye movement (NREM) sleep EEG power and coherence in the sigma band (11–16 Hz) corresponding to spindle activity were also markedly diminished in patients with COS as compared to controls. Furthermore, the degree of deficit in power and coherence of spindles was strongly associated with clinician rated hallucinations and positive symptoms over widespread cortical regions. Our finding of diminished spindle activity and its association with hallucinations likely reflect dysfunction of the thalamocortical circuits in children and adolescents with COS. Given the relative ease of sleep EEG recordings in vulnerable populations, this study highlights the potential of such recordings to characterize brain function in schizophrenia.

The use of single bipolar scalp derivation for the detection of ictal events during long-term EEG monitoring.

Epilepsy is difficult to diagnose using routine EEG recordings of short duration in patients who have low seizure frequency. Long-term EEG may be useful but is impractical in an out-of-hospital setting. We investigated whether single-channel scalp EEG placed behind the earlobe is suitable for seizure identification during prolonged EEG monitoring. Scalp EEG samples were selected from subjects over 15 years of age, and comprised two segments of either background followed by seizure or background followed by background. Bipolar EEG derivations in three directions (F8-T8, C4-T8 and T8-P8) were evaluated for the presence of a seizure by two experienced reviewers. For each EEG segment containing a seizure, one pair of electrodes was oriented towards the suspected region of seizure onset, while two pairs of electrodes were oriented elsewhere. The EEG data contained five frontally localized seizures, five parietal, five temporal, two occipital, and four primary or secondary generalized seizures. The sensitivity and specificity for recognition of seizures was 86% and 95% for Reviewer 1, and 79% and 99% for Reviewer 2, respectively. When identifying a seizure with the lead orientation towards the region of seizure onset, both reviewers identified 20 out of 21 seizures (95%). When the lead was not oriented towards the region of seizure onset, the reviewers identified 34 and 30 out of 42 ictal records correctly, respectively. These results suggest that it is possible to identify epileptic seizures by bipolar EEG derivation using only two scalp electrodes. Lead orientation towards the suspected region of seizure onset is important for optimal detection sensitivity.

Hypoglycemia-induced EEG complexity changes in Type 1 diabetes assessed by fractal analysis algorithm

Abstract In recent years, hypoglycemia-induced changes in the EEG signal of patients with Type 1 diabetes (T1D) have been quantified and studied mainly by linear approaches. So far, sample entropy (SampEn) is the only nonlinear measure used in the literature. SampEn has the disadvantage of being computationally demanding and, hence, difficult to be used in real-time settings. The present study investigates whether other nonlinear indicators, less computationally demanding than SampEn, can be equally sensitive to changes in the EEG signal induced by hypoglycemia. For such a scope, we considered a database obtained from 19 T1D patients who underwent a hyperinsulinemic-hypoglycemic clamp while continuous EEG was recorded. We analyzed the P3-C3 EEG derivation data using three measures of signal complexity based on an approach originally proposed by Higuchi in the 80s: the original measure of fractal dimension and two new indexes based on the Higuchi’s curve. All the three indicators revealed a statistically significant decrease in EEG complexity in the hypo- versus euglycemic state, which is in line with the results previously obtained with SampEn. However, the lower computational cost of the proposed indicators (∼O(N) versus ∼O(N2)) makes them potentially more suited for real-time applications such as the use of EEG to trigger hypoglycemia alerts.

Comparison of epileptic seizure prediction performance for different EEG derivation schemes

Introduction. The influence of EEG derivation scheme on performance of epileptic seizure prediction is considered in the paper. Comparison of epileptic seizure prediction performance for different EEG derivation schemes is conducted. Database of EEG recordings from 20 patients (between 1 and 25 years old) suffering epilepsy was used. Correlation coefficients between channels of signal extracted with different combination of window and preictal lengths were used as features. Support vector machine was used to classify EEG data into interictal and preictal classes. Epileptic seizures prediction performance was evaluated using area under the ROC-curve for two types of derivation schemes: monopolar scheme with reference electrode on ipsilateral ear and scheme with averaged reference. Conclusions. It was found that there is a difference in epileptic seizures prediction performance for different schemes and patients. For subgroup of patients (9 of 20) usage of scheme with reference electrode in ipsilateral ear shows higher performance for window length between 2 and 10 seconds and in entire range of preictal lengths. Scheme with averaged reference shows higher performance when window length is in the range between 90 and 300 seconds.

Electroencephalographic evidence for a respiratory-related cortical activity specific of the preparation of prephonatory breaths

Speech is a major disturbance to automatic breathing control. Speech occurs during exhalation, involving controlled inhibition of automatic inspiration. Additionally, utterances are preceded by prephonatory inspirations that must be prepared to account for prosody and loudness. We hypothesized that the speech-related breathing control activities shaping prephonatory breaths originate in cortical pre-motor areas and should be associated with corresponding EEG evidence. We studied 10 normal subjects (4 men, age 23–27) during spontaneous breathing, sniff manoeuvres, and while reading out loud. Fronto-median inspiratory potentials (Cz EEG derivation) were consistently present before voluntary inspirations and large prephonatory breaths, while these potentials were generally absent during resting breathing or small prephonatory breaths. We conclude that the preparation of prephonatory breaths during speech has a cortical substrate, involving the cortical sources of premotor potentials. These results have important implications to validate whether co-modulation of the pre-motor cortex and breathing during speech are incidental or whether these cortical modulations are necessary for initiation of “speech breathing”.

Hypoglycemia-related electroencephalogram changes assessed by multiscale entropy.

Several clinical studies have shown that low blood glucose (BG) levels affect electroencephalogram (EEG) rhythms through the quantification of traditional indicators based on linear spectral analysis. Nonlinear measures used in the last decades to characterize the EEG in several physiopathological conditions have never been assessed in hypoglycemia. The present study investigates if properties of the EEG signal measured by nonlinear entropy-based algorithms are altered in a significant manner when a state of hypoglycemia is entered. EEG was acquired from 19 patients with type 1 diabetes during a hyperinsulinemic-euglycemic-hypoglycemic clamp experiment. In parallel, BG was frequently monitored by the standard YSI glucose and lactate analyzer and used to identify two 1-h intervals corresponding to euglycemia and hypoglycemia, respectively. In each subject, the P3-C3 EEG derivation in the two glycemic intervals was assessed using the multiscale entropy (MSE) approach, obtaining measures of sample entropy (SampEn) at various temporal scales. The comparison of how signal irregularity measured by SampEn varies as the temporal scale increases in the two glycemic states provides information on how EEG complexity is affected by hypoglycemia. For both glycemic states, the MSE analysis showed that SampEn increases at small time scales and then monotonically decreases as the time scale becomes larger. Comparing the two conditions, SampEn was higher in hypoglycemia only at medium time scales. A decrease in the complexity of EEG occurs when a state of hypoglycemia is entered, because of a degradation of the EEG long-range temporal correlations. Thanks to its ability to assess nonlinear dynamics of the EEG signal, the MSE approach seems to be a useful tool to complement information brought by standard linear indicators and provide new insights on how hypoglycemia affects brain functioning.

Development of EEG Amplifier and Derivation of BIS Algorithm

The raw EEG signals are captured from the brain, specifically Delta waves. The signal is amplified, rectified and processed mathematically. Using FFT EEG parameters are drawn which are further processed with complex algorithm to result in BIS score ranging from 0 to 100. Using this score the depth of anesthesia is calculated.

Early and Later Life Stress Alter Brain Activity and Sleep in Rats

Exposure to early life stress may profoundly influence the developing brain in lasting ways. Neuropsychiatric disorders associated with early life adversity may involve neural changes reflected in EEG power as a measure of brain activity and disturbed sleep. The main aim of the present study was for the first time to characterize possible changes in adult EEG power after postnatal maternal separation in rats. Furthermore, in the same animals, we investigated how EEG power and sleep architecture were affected after exposure to a chronic mild stress protocol. During postnatal day 2–14 male rats were exposed to either long maternal separation (180 min) or brief maternal separation (10 min). Long maternally separated offspring showed a sleep-wake nonspecific reduction in adult EEG power at the frontal EEG derivation compared to the brief maternally separated group. The quality of slow wave sleep differed as the long maternally separated group showed lower delta power in the frontal-frontal EEG and a slower reduction of the sleep pressure. Exposure to chronic mild stress led to a lower EEG power in both groups. Chronic exposure to mild stressors affected sleep differently in the two groups of maternal separation. Long maternally separated offspring showed more total sleep time, more episodes of rapid eye movement sleep and higher percentage of non-rapid eye movement episodes ending in rapid eye movement sleep compared to brief maternal separation. Chronic stress affected similarly other sleep parameters and flattened the sleep homeostasis curves in all offspring. The results confirm that early environmental conditions modulate the brain functioning in a long-lasting way.

Dissipation of sleep pressure is stable across adolescence

The sleep electroencephalogram (EEG) undergoes many changes during adolescence. We assessed whether sleep homeostasis is altered across adolescent development using two measures: the dissipation of slow-wave activity (SWA, 0.6–4.6Hz) across the night and the rate of build-up of SWA in the first non-rapid eye movement (NREM) sleep episode. Furthermore, we examined the association between homeostatic and circadian measures, by correlating the build-up of SWA in the first non-rapid eye movement (NREM) sleep episode with circadian phase. Finally, we compared the dissipation of SWA in individuals with (PH+) and without (PH−) a parental history of alcohol abuse/dependence. Twenty children (8 PH+) and 25 teens (10 PH+) underwent two consecutive polysomnographic recordings at ages 9/10 and 15/16years and again 1.5–3years later. Thirteen young adults (ages 20–23 years; no PH+) were assessed one time. The decay of Process S was modeled for each individual at each assessment using data from both recordings. Four parameters of Process S were derived for EEG derivation C3/A2: time constant of the decay, lower asymptote (LA), the level of S at sleep onset (SSO), and SSO minus LA. We found no change in these parameters between assessments for the children and teen cohorts. Between-subject analysis of the follow-up assessment for children (ages 11–13 years) and the initial assessment for teens (ages 15/16 years) showed no difference in these parameters, nor did follow-up assessment of teens (ages 17–19 years) compared to the single assessment of young adults (ages 20–23 years). Similarly, we observed no developmental changes in the rate of the build-up of SWA in the first NREM sleep episode for our within- and between-subject analyses, or a correlation between this measure and circadian phase for either cohort. With regard to parental alcohol history, we found no difference in the dissipation of sleep pressure between PH+ and PH− children and teens. These results indicate that the dissipation of sleep pressure does not change across adolescent development, is not correlated with circadian phase, and does not differ between PH+ and PH− children and teens.

The 2007 AASM recommendations for EEG electrode placement in polysomnography: impact on sleep and cortical arousal scoring.

To examine the impact of using American Academy of Sleep Medicine (AASM) recommended EEG derivations (F4/M1, C4/M1, O2/M1) vs. a single derivation (C4/M1) in polysomnography (PSG) on the measurement of sleep and cortical arousals, including inter- and intra-observer variability. Prospective, non-blinded, randomized comparison. Three Australian tertiary-care hospital clinical sleep laboratories. 30 PSGs from consecutive patients investigated for obstructive sleep apnea (OSA) during December 2007 and January 2008. N/A. To examine the impact of EEG derivations on PSG summary statistics, 3 scorers from different Australian clinical sleep laboratories each scored separate sets of 10 PSGs twice, once using 3 EEG derivations and once using 1 EEG derivation. To examine the impact on inter- and intra-scorer reliability, all 3 scorers scored a subset of 10 PSGs 4 times, twice using each method. All PSGs were de-identified and scored in random order according to the 2007 AASM Manual for the Scoring of Sleep and Associated Events. Using 3 referential EEG derivations during PSG, as recommended in the AASM manual, instead of a single central EEG derivation, as originally suggested by Rechtschaffen and Kales (1968), resulted in a mean ± SE decrease in N1 sleep of 9.6 ± 3.9 min (P = 0.018) and an increase in N3 sleep of 10.6 ± 2.8 min (P = 0.001). No significant differences were observed for any other sleep or arousal scoring summary statistics; nor were any differences observed in inter-scorer or intra-scorer reliability for scoring sleep or cortical arousals. This study provides information for those changing practice to comply with the 2007 AASM recommendations for EEG placement in PSG, for those using portable devices that are unable to comply with the recommendations due to limited channel options, and for the development of future standards for PSG scoring and recording. As the use of multiple EEG derivations only led to small changes in the distribution of derived sleep stages and no significant differences in scoring reliability, this study calls into question the need to use multiple EEG derivations in clinical PSG as suggested in the AASM manual.

EEG Delta Power Decline Can Begin Before Age 11: A Reply to Campbell and Feinberg

CAMPBELL AND FEINBERG1 QUESTION OUR CONCLUSION THAT THE ADOLESCENT SLEEP EEG POWER DECLINE CAN OCCUR BEFORE AGE 11 YEARS BY presenting averaged data from children whose sleep was studied on unspecified “usual” schedules. They state that delta power decline begins after age 11 and hypothesize that this age “…signals the onset of brain adolescence” characterized by “…massive, parallel declines in cerebral metabolic rate, synaptic density and delta wave amplitude.”1 Findings from neuroanatomical studies suggest that defining the onset of cortical reorganization in the healthy maturing brain is not so straightforward. By stating that this process does not begin until after age 11, Campbell and Feinberg overlook such important factors as (1) brain maturation occurs at different times in different cortical regions and (2) the timing of maturation is associated with significant individual variability (in part related to sex2). Studies using MRI,2,3 postmortem synaptic density,4 waking EEG,5 and PET6 indicate developmental differences among brain regions. For example, an MRI study showed peak cortical thickness at different ages in different regions3; a corollary is that the age-related decline began at different ages for different regions. Decline in gray matter volume is thought to reflect the elimination of synapses, seen as reduced EEG amplitude.5 Furthermore, Feinberg et al.7 have modeled the maturational curves for delta wave amplitude in the sleep EEG showing the age of peak delta wave amplitude at 7.4 years. The recent Campbell and Feinberg8 data derive from a single EEG derivation C3/A2 or C4/A1 (used interchangeably), which lies over the postcentral gyrus.9 Shaw et al.3 estimated 8.5 years as the age of peak cortical thickness in this region. Our data,10 collected under conditions where prior sleep and in-lab time in bed were held constant, support the notion that the sleep EEG can manifest cortical maturation at an early age. For example, we found a 55% reduction in delta power and a 52% decline in overall power (PΔ of −44%/year, Figure 1)10 at C3/A2 for one boy from age 9.4 to 11.2 years. Furthermore, the decline in sleep EEG power occurred for frequencies up to 16 Hz (Figure 4).10 Our data also showed that the sleep EEG power changes differed regionally (see Figures 2 and 4).10 Our findings show that the maturational timing varies, cannot be pinpointed to a specific age, and can begin before age 11. The individual variability shown in Figure 1 and Table 110 emphasizes this point. Indeed, large individual differences appear in Campbell and Feinberg's8 Figure 2, where several participants show decreases of delta power before age 11 and others large increases in delta power after age 11. We conclude by congratulating Campbell and Feinberg on their longitudinal studies and Feinberg for his pioneering work. Our comments were meant to highlight methodological differences in control of such variables as prior sleep and sleeping environment that impact the sleep EEG and may account for the observed differences. Finally, neither our data set nor that of Campbell and Feinberg8 can provide a definitive statement about the onset of “brain adolescence” without including younger children in whom synaptic pruning has not yet begun.

Temporal coding of brain patterns for direct limb control in humans

For individuals with a high spinal cord injury (SCI) not only the lower limbs, but also the upper extremities are paralyzed. A neuroprosthesis can be used to restore the lost hand and arm function in those tetraplegics. The main problem for this group of individuals, however, is the reduced ability to voluntarily operate device controllers. A brain–computer interface provides a non-manual alternative to conventional input devices by translating brain activity patterns into control commands. We show that the temporal coding of individual mental imagery pattern can be used to control two independent degrees of freedom – grasp and elbow function – of an artificial robotic arm by utilizing a minimum number of EEG scalp electrodes. We describe the procedure from the initial screening to the final application. From eight naïve subjects participating online feedback experiments, four were able to voluntarily control an artificial arm by inducing one motor imagery pattern derived from one EEG derivation only.

1A2-D10 BCIに用いる頭皮上脳波計測用アクティブ電極および周辺機器の開発

This research aims at development of the BCI system which performs apparatus control of a computer, a robot arm, etc., and the electrode used for EEG derivation. BCI is a direct communication pathway between a human or animal brain and an external device. A multi channel active electrode with the headgear shape using the voltage follower was made in order to do measurement of brain waves in simple way this time, so I'll report on this.

Overt foot movement detection in one single Laplacian EEG derivation

In this work one single Laplacian derivation and a full description of band power values in a broad frequency band are used to detect brisk foot movement execution in the ongoing EEG. Two support vector machines (SVM) are trained to detect the event-related desynchronization (ERD) during motor execution and the following beta rebound (event-related synchronization, ERS) independently. Their performance is measured through the simulation of an asynchronous brain switch. ERS (true positive rate = 0.74 ± 0.21) after motor execution is shown to be more stable than ERD (true positive rate = 0.21 ± 0.12). A novel combination of ERD and post-movement ERS is introduced. The SVM outputs are combined with a product rule to merge ERD and ERS detection. For this novel approach the average information transfer rate obtained was 11.19 ± 3.61 bits/min.

The Visual Scoring of Sleep and Arousal in Infants and Children

Age is probably the single most crucial factor determining how humans sleep. Age and level of vigilance significantly influence the electroencephalogram (EEG) and the polysomnogram (PSG). The Pediatric Task Force provide an evidence-based review of the age-related development of the polysomnographic features of sleep in neonates, infants, and children, assessing the reliability and validity of these features, and assessing alternative methods of measurement. We used this annotated supporting text to develop rules for scoring sleep and arousals in infants and children. A pediatric EEG or PSG can only be determined to be normal by assessing whether the EEG patterns are appropriate for maturational age. Sleep in infants at term can be scored as NREM and REM sleep because all the polysomnographic and EEG features of REM sleep are present and quiet sleep, if not NREM sleep, is at least "not REM sleep." The dominant posterior rhythm (DPR) of relaxed wakefulness increases in frequency with age: (1) 3.5-4.5 Hz in 75% of normal infants by 3-4 months post-term; (2) 5-6 Hz in most infants 5-6 months post-term; 3) 6 Hz in 70% of normal children by 2 months of age; and 3) 8 Hz (range 7.5-9.5 Hz) in 82% of normal children age 3 years, 9 Hz in 65% of 9-year-olds, and 10 Hz in 65% of 15-year-old controls. Sleep spindles in children occur independently at two different frequencies and two different scalp locations: 11.0-12.75 Hz over the frontal and 13.0-14.75 Hz over the centroparietal electrodes; these findings are most prominent in children younger than 13 years. Centroparietal spikes are often maximal over the vertex (Cz), less often maximal over the left central (C3) or right central (C4) EEG derivation. About 50% of sleep spindles within a particular infant's PSG are asynchronous before 6 months of age, 30% at 1 year. Based on this, we recommend that: (1) sleep spindles be scored as a polysomnographic signature of NREM stage 2 sleep (N2) at whatever age they are first seen in a PSG, typically present by 2 to 3 months post-term; (2) identify and score sleep spindles from the frontal and centroparietal EEG derivations, especially in infants and children younger than 13 years. NREM sleep in an infant or child can be scored if the dominant posterior rhythm occupies 20% of the 30-second epoch contain 0.5 to 2 Hz >75 microV (usually 100-400 microV) activity as N3. The DPR should be scored in the EEG channel that is best observed, (typically occipital), but DPR reactive to eye opening can be seen in central electrodes. Because sleep spindles occur independently over the frontal and central regions in children, they should be scored whether they occur in the frontal or central regions. Because sleep spindles are asynchronous before age 2 years, simultaneous recording of left and right frontal and central activity may be warranted in children 1-2 years of age. Simultaneous recording of left, right, and midline central electrodes may be appropriate because of the asynchronous nature of sleep spindles before age 2 years, but reliability testing is needed. Evidence has shown that the PSG cannot reliably be used to identify neurological deficits or to predict behavior or outcome in infants because of significant diversity of results, even in normal infants. Normal sleep EEG patterns and architecture are present in the first year of life, even in infants with severe neurological compromise. Increasing evidence suggests that sleep and its disorders play critical roles in the development of healthy children and healthy adults thereafter. Reliability studies comparing head-to-head different scoring criteria, recording techniques, and derivations are needed so that future scoring recommendations can be based on evidence rather than consensus opinion. We need research comparing clinical outcomes with PSG measures to better inform clinicians and families exactly what meaning a PSG has in evaluating a child's suspected sleep disorder.

Systematic performance evaluation of a continuous-scale sleep depth measure

In this article, systematic performance evaluation of a continuous-scale sleep depth measure will be discussed. Our main objective has been to select the adjustable analysis parameters such that the best possible correspondence between method output and standard visual sleep staging could be achieved. Sleep depth estimation was based on continuous monitoring of short-time EEG synchronization through the local mean frequency of the EEG. During the experiments, total amount of 752 different combinations of four adjustable parameters were compared based on all-night sleep EEG recordings of 15 healthy subjects. Optimization strategy applied was based on maximizing the weighted average of pair-wise separabilities of EEG mean frequency distributions in all the standard sleep stage pairs. Finally, robustness of the optimized parameters was verified with an independent dataset of 34 all-night sleep recordings. Our results show that clear topological differences between brain hemispheres and different electrode locations exist. Performance improvements of even 20–30% units can be achieved by proper selection of analysis parameters and the EEG derivation used for the analysis. Remarkable independence of system performance on the analysis window length leads to improved temporal resolution compared to that achieved through standard visual analysis. In addition to giving practical suggestions on the parameter selection, we also propose a possible method for improving stage separability especially between S2 and REM.

Spatio-temporal decomposition of the electroencephalogram in patients with cirrhosis

Slowing of the electroencephalogram (EEG) is a recognised feature of hepatic encephalopathy but its diagnostic sensitivity is indeterminate. Recent advances in EEG analysis should provide better quantifiable/more informative data. The aim of this study was to isolate and determine the scalp distribution of the posterior basic rhythm, in patients with cirrhosis, using a technique for spatio-temporal decomposition (SEDACA) of the EEG. One hundred and ten patients with cirrhosis, classified, using clinical and psychometric criteria, as neuropsychiatrically unimpaired or as having minimal/overt hepatic encephalopathy were studied. Eyes-closed, awake EEGs were obtained and subjected to standard spectral analysis and spatio-temporal decomposition. Control data were obtained from 26 reference EEGs. The error in the estimate of the SEDACA-derived mean dominant frequency was lower than for the standard EEG derivation (P<0.00001). The SEDACA-derived spectral estimates correlated better with neuropsychiatric status and allowed differentiation of the patients with minimal hepatic encephalopathy from the reference population. The SEDACA-derived spatial information showed an anteriorization of the posterior basic rhythm, which became more prominent as the degree of neuropsychiatric impairment increased (P=0.00052). Analysis of the EEG utilising SEDACA provides significantly more diagnostic information on the neuropsychiatric status of patients with cirrhosis than obtained conventionally.